Charles H. Greenberg

Kinetics of Cerebral Amyloid Angiopathy Progression in a Transgenic Mouse Model of Alzheimer Disease

Cerebral amyloid angiopathy (CAA), the deposition of cerebrovascular β-amyloid (Aβ) in the walls of arterial vessels, has been implicated in hemorrhagic stroke and is present in most cases of Alzheimer disease. Previous studies of the progression of CAA in humans and animal models have been limited to the comparison of pathological tissue from different brains at single time points. Our objective was to visualize in real time the initiation and progression of CAA in Tg2576 mice by multiphoton microscopy through cranial windows. Affected vessels were labeled by methoxy-X04, a fluorescent dye that selectively binds cerebrovascular β-amyloid and plaques. With serial imaging sessions spaced at weekly intervals, we were able to observe the earliest appearance of CAA in leptomeningeal arteries as multifocal deposits of band-like Aβ. Over subsequent imaging sessions, we were able to identify growth of these deposits (propagation), as well as appearance of new bands (additional initiation events). Statistical modeling of the data suggested that as the extent of CAA progressed in this vascular bed, there was increased prevalence of propagation over initiation. During the early phases of CAA development, the overall pathology burden progressed at a rate of 0.35% of total available vessel area per day (95% confidence interval, 0.3–0.4%). The consistent rate of disease progression implies that this model is amenable to investigations of therapeutic interventions.

Fabs enable single particle cryoEM studies of small proteins

In spite of its recent achievements, the technique of single particle electron cryomicroscopy (cryoEM) has not been widely used to study proteins smaller than 100 kDa, although it is a highly desirable application of this technique. One fundamental limitation is that images of small proteins embedded in vitreous ice do not contain adequate features for accurate image alignment. We describe a general strategy to overcome this limitation by selecting a fragment antigen binding (Fab) to form a stable and rigid complex with a target protein, thus providing a defined feature for accurate image alignment. Using this approach, we determined a three-dimensional structure of an ∼65 kDa protein by single particle cryoEM. Because Fabs can be readily generated against a wide range of proteins by phage display, this approach is generally applicable to study many small proteins by single particle cryoEM.

Molecular architecture of the yeast Mediator complex

The 21-subunit Mediator complex transduces regulatory information from enhancers to promoters, and performs an essential role in the initiation of transcription in all eukaryotes. Structural information on two-thirds of the complex has been limited to coarse subunit mapping onto 2-D images from electron micrographs. We have performed chemical cross-linking and mass spectrometry, and combined the results with information from X-ray crystallography, homology modeling, and cryo-electron microscopy by an integrative modeling approach to determine a 3-D model of the entire Mediator complex. The approach is validated by the use of X-ray crystal structures as internal controls and by consistency with previous results from electron microscopy and yeast two-hybrid screens. The model shows the locations and orientations of all Mediator subunits, as well as subunit interfaces and some secondary structural elements. Segments of 20–40 amino acid residues are placed with an average precision of 20 Å. The model reveals roles of individual subunits in the organization of the complex. DOI: http://dx.doi.org/10.7554/eLife.08719.001

Ring closure activates yeast γTuRC for species-specific microtubule nucleation

The γ-tubulin ring complex (γTuRC) is the primary microtubule nucleator in cells. γγTuRC is assembled from repeating γγ-tubulin small complex (γTuSC) subunits and is thought to function as a template by presenting a γ-tubulin ring that mimics microtubule geometry. However, a previous yeast γTuRC structure showed γTuSC in an open conformation that prevents matching to microtubule symmetry. By contrast, we show here that γ-tubulin complexes are in a closed conformation when attached to microtubules. To confirm the functional importance of the closed γTuSC ring, we trapped the closed state and determined its structure, showing that the γ-tubulin ring precisely matches microtubule symmetry and providing detailed insight into γTuRC architecture. Importantly, the closed state is a stronger nucleator, thus suggesting that this conformational switch may allosterically control γTuRC activity. Finally, we demonstrate that γTuRCs have a strong preference for tubulin from the same species.

Modeling of proteins and their assemblies with the Integrative Modeling Platform.

To understand the workings of the living cell, we need to characterize protein assemblies that constitute the cell (for example, the ribosome, 26S proteasome, and the nuclear pore complex). A reliable high-resolution structural characterization of these assemblies is frequently beyond the reach of current experimental methods, such as X-ray crystallography, NMR spectroscopy, electron microscopy, footprinting, chemical cross-linking, FRET spectroscopy, small-angle X-ray scattering, and proteomics. However, the information garnered from different methods can be combined and used to build computational models of the assembly structures that are consistent with all of the available datasets. Here, we describe a protocol for this integration, whereby the information is converted to a set of spatial restraints and a variety of optimization procedures can be used to generate models that satisfy the restraints as much as possible. These generated models can then potentially inform about the precision and accuracy of structure determination, the accuracy of the input datasets, and further data generation. We also demonstrate the Integrative Modeling Platform (IMP) software, which provides the necessary computational framework to implement this protocol, and several applications for specific-use cases.

Modeling Intracerebral Hemorrhage Growth and Response to Anticoagulation

The mechanism for hemorrhage enlargement in the brain, a key determinant of patient outcome following hemorrhagic stroke, is unknown. We performed computer-based stochastic simulation of one proposed mechanism, in which hemorrhages grow in “domino” fashion via secondary shearing of neighboring vessel segments. Hemorrhages were simulated by creating an initial site of primary bleeding and an associated risk of secondary rupture at adjacent sites that decayed over time. Under particular combinations of parameters for likelihood of secondary rupture and time-dependent decay, a subset of lesions expanded, creating a bimodal distribution of microbleeds and macrobleeds. Systematic variation of the model to simulate anticoagulation yielded increases in both macrobleed occurrence (26.9%, 53.2%, and 70.0% of all hemorrhagic events under conditions simulating no, low-level, and high-level anticoagulation) and final hemorrhage size (median volumes 111, 276, and 412 under the same three conditions), consistent with data from patients with anticoagulant-related brain hemorrhages. Reversal from simulated high-level anticoagulation to normal coagulation was able to reduce final hemorrhage size only if applied relatively early in the course of hemorrhage expansion. These findings suggest that a model based on a secondary shearing mechanism can account for some of the clinically observed properties of intracerebral hemorrhage, including the bimodal distribution of volumes and the enhanced hemorrhage growth seen with anticoagulation. Future iterations of this model may be useful for elucidating the effects of hemorrhage growth of factors related to secondary shearing (such as small vessel pathology) or time-dependent decay (such as hemostatic agents).

Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome

The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation. As aberration in proteasomal degradation has been implicated in many human diseases, structural analysis of the human 26S proteasome complex is essential to advance our understanding of its action and regulation mechanisms. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for elucidating structural topologies of large protein assemblies, with its unique capability of studying protein complexes in cells. To facilitate the identification of cross-linked peptides, we have previously developed a robust amine reactive sulfoxide-containing MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). To better understand the structure and regulation of the human 26S proteasome, we have established new DSSO-based in vivo and in vitro XL-MS workflows by coupling with HB-tag based affinity purification to comprehensively examine protein-protein interactions within the 26S proteasome. In total, we have identified 447 unique lysine-to-lysine linkages delineating 67 interprotein and 26 intraprotein interactions, representing the largest cross-link dataset for proteasome complexes. In combination with EM maps and computational modeling, the architecture of the 26S proteasome was determined to infer its structural dynamics. In particular, three proteasome subunits Rpn1, Rpn6, and Rpt6 displayed multiple conformations that have not been previously reported. Additionally, cross-links between proteasome subunits and 15 proteasome interacting proteins including 9 known and 6 novel ones have been determined to demonstrate their physical interactions at the amino acid level. Our results have provided new insights on the dynamics of the 26S human proteasome and the methodologies presented here can be applied to study other protein complexes.

Integrative structure and functional anatomy of a nuclear pore complex

Nuclear pore complexes play central roles as gatekeepers of RNA and protein transport between the cytoplasm and nucleoplasm. However, their large size and dynamic nature have impeded a full structural and functional elucidation. Here we determined the structure of the entire 552-protein nuclear pore complex of the yeast Saccharomyces cerevisiae at sub-nanometre precision by satisfying a wide range of data relating to the molecular arrangement of its constituents. The nuclear pore complex incorporates sturdy diagonal columns and connector cables attached to these columns, imbuing the structure with strength and flexibility. These cables also tie together all other elements of the nuclear pore complex, including membrane-interacting regions, outer rings and RNA-processing platforms. Inwardly directed anchors create a high density of transport factor-docking Phe-Gly repeats in the central channel, organized into distinct functional units. This integrative structure enables us to rationalize the architecture, transport mechanism and evolutionary origins of the nuclear pore complex.

Integrative structure modeling with the Integrative Modeling Platform

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.

Integrative structure modeling with the Integrative Modeling Platform: Integrative Structure Modeling with IMP

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.